{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Exploratory_data_Analysis_2.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Tanu-N-Prabhu/Python/blob/master/Exploratory%20Data%20Analysis/Exploratory_data_Analysis_2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gCmEPdNniJqV",
        "colab_type": "text"
      },
      "source": [
        "# Exploring the data using python."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SloBpm7ViVLK",
        "colab_type": "text"
      },
      "source": [
        "## In this tutorial, we will use the exploratory data analysis approach to summarize and analyze the main characteristics of a cars data set."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TXgCY_shiQS6",
        "colab_type": "text"
      },
      "source": [
        "![alt text](https://www.statistika.co/images/services/Exploratory%20Data%20Analysis%20-%20EDA%201000x468.jpg)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JvYC7hU6iX5z",
        "colab_type": "text"
      },
      "source": [
        "## Let us understand how to explore the data using python and later build a machine learning model on that data in the next tutorial."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "50MDC448xr45",
        "colab_type": "text"
      },
      "source": [
        "## 1) Choosing a Data Set\n",
        "\n",
        "I chose a data-set titled “Cars” data from Kaggle the author of this data set is Lilit Janughazyan [1]. From my childhood, I was interested in and fascinated about cars. I still remember I used to maintain a book wherein I used to stick all the pictures of different cars along with its specifications. I was more up to date about the latest cars and their specifications. I was more like a specs sheet remembering almost all information about cars explaining people about different cars available in the market. And it was my dream when I was young that I wanted to predict the prices of cars given its specifications. With the help of this interest, I wanted to choose a data set based on Cars in this assignment. I wanted to fulfill my dream of creating a model that would be fed with the specifications of the cars such as Horsepower, Cylinders or Engine Size, and then the model should predict the price of the car based on these specifications. The data set can be found here: [Cars dataset](https://www.kaggle.com/ljanjughazyan/cars1)\n",
        "\n",
        "The main reason for me choosing the data set over others was that there were almost 110 data sets about cars under the most voted category in Kaggle (the most voted meaning the best and famous collection of data sets that are available on Kaggle) almost all these data sets had one or the other features missing. For example, the data set “Automobile Data set” [2] had most features of a car but did not have a price feature in it, which is the most important feature according to my interest. Hence I took a lot of time short-listing many data sets and then I concluded the “Cars” data set because this data set had almost every important feature of a car such as Horsepower, MSRP, Invoice, Cylinders, Engine Size and many more because of these good features this was the main reason I chose this data set over the other data sets available in Kaggle.\n",
        "\n",
        "\n",
        "This data set was straightaway stored in a CSV (Comma Separated Value) format on Kaggle. I did not have to perform any operations to get the data into a format. Since the data was already in a CSV format it needed very little work to import the data set all I had to do is just download, read the CSV data and store it in a pandas data frame, for this I had to import pandas library. \n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Mt4xqcgxxzhC",
        "colab_type": "text"
      },
      "source": [
        "## 2) Obtaining the data\n",
        "\n",
        "To get or load the dataset into the notebook, all I did was one trivial step. In Google Colab at the left-hand side of the notebook, you will find a “> “(greater than symbol). On clicking that you will find a tab with three options, out of which you have to select Files. Then you can easily upload your dataset with the help of the Upload option. No need to mount to the google drive or use any specific libraries just upload the data set and your job is done. This is how I got the dataset into my notebook.\n",
        "\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9TNCVjPEx4r8",
        "colab_type": "text"
      },
      "source": [
        "## 3) Scrubbing and Formatting\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "liisRwyOx8UP",
        "colab_type": "text"
      },
      "source": [
        "**Formatting the data into a data frame**\n",
        "\n",
        "Since the data set was already in a CSV format. All I had to do is just format the data into a pandas data frame. This was done by using a pandas data frame method called (read_csv) by importing pandas library. The read_csv data frame method was used by passing the filename as an argument. And then by executing this, it converted the CSV file into a neatly organized pandas data frame format.\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Go7GwlNfI-92",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Importing the required libraries\n",
        "import pandas as pd               \n",
        "import numpy as np\n",
        "import seaborn as sns                       #visualisation\n",
        "import matplotlib.pyplot as plt             #visualisation\n",
        "%matplotlib inline     \n",
        "sns.set(color_codes=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7qfGWlMOJH2g",
        "colab_type": "code",
        "outputId": "d036db7b-fe19-4be3-fb0f-e41e772e843f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 219
        }
      },
      "source": [
        "# Loading the CSV file into a pandas dataframe.\n",
        "df = pd.read_csv(\"CARS.csv\")\n",
        "df.head(5)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Make</th>\n",
              "      <th>Model</th>\n",
              "      <th>Type</th>\n",
              "      <th>Origin</th>\n",
              "      <th>DriveTrain</th>\n",
              "      <th>MSRP</th>\n",
              "      <th>Invoice</th>\n",
              "      <th>EngineSize</th>\n",
              "      <th>Cylinders</th>\n",
              "      <th>Horsepower</th>\n",
              "      <th>MPG_City</th>\n",
              "      <th>MPG_Highway</th>\n",
              "      <th>Weight</th>\n",
              "      <th>Wheelbase</th>\n",
              "      <th>Length</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Acura</td>\n",
              "      <td>MDX</td>\n",
              "      <td>SUV</td>\n",
              "      <td>Asia</td>\n",
              "      <td>All</td>\n",
              "      <td>$36,945</td>\n",
              "      <td>$33,337</td>\n",
              "      <td>3.5</td>\n",
              "      <td>6.0</td>\n",
              "      <td>265</td>\n",
              "      <td>17</td>\n",
              "      <td>23</td>\n",
              "      <td>4451</td>\n",
              "      <td>106</td>\n",
              "      <td>189</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Acura</td>\n",
              "      <td>RSX Type S 2dr</td>\n",
              "      <td>Sedan</td>\n",
              "      <td>Asia</td>\n",
              "      <td>Front</td>\n",
              "      <td>$23,820</td>\n",
              "      <td>$21,761</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>200</td>\n",
              "      <td>24</td>\n",
              "      <td>31</td>\n",
              "      <td>2778</td>\n",
              "      <td>101</td>\n",
              "      <td>172</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Acura</td>\n",
              "      <td>TSX 4dr</td>\n",
              "      <td>Sedan</td>\n",
              "      <td>Asia</td>\n",
              "      <td>Front</td>\n",
              "      <td>$26,990</td>\n",
              "      <td>$24,647</td>\n",
              "      <td>2.4</td>\n",
              "      <td>4.0</td>\n",
              "      <td>200</td>\n",
              "      <td>22</td>\n",
              "      <td>29</td>\n",
              "      <td>3230</td>\n",
              "      <td>105</td>\n",
              "      <td>183</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Acura</td>\n",
              "      <td>TL 4dr</td>\n",
              "      <td>Sedan</td>\n",
              "      <td>Asia</td>\n",
              "      <td>Front</td>\n",
              "      <td>$33,195</td>\n",
              "      <td>$30,299</td>\n",
              "      <td>3.2</td>\n",
              "      <td>6.0</td>\n",
              "      <td>270</td>\n",
              "      <td>20</td>\n",
              "      <td>28</td>\n",
              "      <td>3575</td>\n",
              "      <td>108</td>\n",
              "      <td>186</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Acura</td>\n",
              "      <td>3.5 RL 4dr</td>\n",
              "      <td>Sedan</td>\n",
              "      <td>Asia</td>\n",
              "      <td>Front</td>\n",
              "      <td>$43,755</td>\n",
              "      <td>$39,014</td>\n",
              "      <td>3.5</td>\n",
              "      <td>6.0</td>\n",
              "      <td>225</td>\n",
              "      <td>18</td>\n",
              "      <td>24</td>\n",
              "      <td>3880</td>\n",
              "      <td>115</td>\n",
              "      <td>197</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Make           Model   Type Origin  ... MPG_Highway Weight Wheelbase  Length\n",
              "0  Acura             MDX    SUV   Asia  ...          23   4451       106     189\n",
              "1  Acura  RSX Type S 2dr  Sedan   Asia  ...          31   2778       101     172\n",
              "2  Acura         TSX 4dr  Sedan   Asia  ...          29   3230       105     183\n",
              "3  Acura          TL 4dr  Sedan   Asia  ...          28   3575       108     186\n",
              "4  Acura      3.5 RL 4dr  Sedan   Asia  ...          24   3880       115     197\n",
              "\n",
              "[5 rows x 15 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lMKhC3g4Qkc6",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_mdx5cImyBW0",
        "colab_type": "text"
      },
      "source": [
        "**Determining instances and the number of features.**\n",
        "\n",
        "This data set has 428 instances and 15 features also called as rows and columns. The instances here represent different car brands such as BMW, Mercedes, Audi, and 35 more, features represent Make, Model, Type, Origin, Drive Train, MSRP, Invoice, Engine Size, Cylinders, Horsepower, MPG-City, MPG-Highway, Weight, Wheelbase, and Length of the car.\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W5l_KJy_yFVu",
        "colab_type": "text"
      },
      "source": [
        "**Removing irrelevant features.**\n",
        "\n",
        "I will remove some features such as Drive Train, Model, Invoice, Type, and Origin from this dataset. Because these features do not contribute to the prediction of price. As of now, I will remove the Drive Train, the Drive Train will not support for predicting the price of the car because most of the cars in this data set were front-wheel drive (52.8%) and the rest were rear wheel and all wheel drive. \n",
        "\n",
        "Similarly, the model, type and origin are irrelevant and are not needed in this context, it’s the brand which is important not the model of the car, and when it comes to type of the car most of the cars were of type Sedan and I kept the weight and length features of the cars in which case I can easily determine whether if it’s an SUV, Sedan or a truck. I will also be removing the Invoice feature of the car because I have the MSRP as the price I don't need the invoice because having any one type of price of the car makes more sense and it prevents in leading in ambiguous results (because both MSRP and Invoice are very closely related and you cannot predict the MSRP given the invoice). Lastly, the origin of cars has nothing to do with the prediction rate so I had to remove it and most of the cars were originated from Europe.\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s_QtFxfrJL8L",
        "colab_type": "code",
        "outputId": "ca650cd9-c9b7-4a9c-c3ad-94fd9ccdaa4d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        }
      },
      "source": [
        "# Removing irrelevant features\n",
        "df = df.drop(['Model','DriveTrain','Invoice', 'Origin', 'Type'], axis=1)\n",
        "df.head(5)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Make</th>\n",
              "      <th>MSRP</th>\n",
              "      <th>EngineSize</th>\n",
              "      <th>Cylinders</th>\n",
              "      <th>Horsepower</th>\n",
              "      <th>MPG_City</th>\n",
              "      <th>MPG_Highway</th>\n",
              "      <th>Weight</th>\n",
              "      <th>Wheelbase</th>\n",
              "      <th>Length</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Acura</td>\n",
              "      <td>$36,945</td>\n",
              "      <td>3.5</td>\n",
              "      <td>6.0</td>\n",
              "      <td>265</td>\n",
              "      <td>17</td>\n",
              "      <td>23</td>\n",
              "      <td>4451</td>\n",
              "      <td>106</td>\n",
              "      <td>189</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Acura</td>\n",
              "      <td>$23,820</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>200</td>\n",
              "      <td>24</td>\n",
              "      <td>31</td>\n",
              "      <td>2778</td>\n",
              "      <td>101</td>\n",
              "      <td>172</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Acura</td>\n",
              "      <td>$26,990</td>\n",
              "      <td>2.4</td>\n",
              "      <td>4.0</td>\n",
              "      <td>200</td>\n",
              "      <td>22</td>\n",
              "      <td>29</td>\n",
              "      <td>3230</td>\n",
              "      <td>105</td>\n",
              "      <td>183</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Acura</td>\n",
              "      <td>$33,195</td>\n",
              "      <td>3.2</td>\n",
              "      <td>6.0</td>\n",
              "      <td>270</td>\n",
              "      <td>20</td>\n",
              "      <td>28</td>\n",
              "      <td>3575</td>\n",
              "      <td>108</td>\n",
              "      <td>186</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Acura</td>\n",
              "      <td>$43,755</td>\n",
              "      <td>3.5</td>\n",
              "      <td>6.0</td>\n",
              "      <td>225</td>\n",
              "      <td>18</td>\n",
              "      <td>24</td>\n",
              "      <td>3880</td>\n",
              "      <td>115</td>\n",
              "      <td>197</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Make     MSRP  EngineSize  ...  Weight  Wheelbase  Length\n",
              "0  Acura  $36,945         3.5  ...    4451        106     189\n",
              "1  Acura  $23,820         2.0  ...    2778        101     172\n",
              "2  Acura  $26,990         2.4  ...    3230        105     183\n",
              "3  Acura  $33,195         3.2  ...    3575        108     186\n",
              "4  Acura  $43,755         3.5  ...    3880        115     197\n",
              "\n",
              "[5 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FaGwmCfyQz9O",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xQ_-zvL2yLqK",
        "colab_type": "text"
      },
      "source": [
        "## 4) Exploratory Data Analysis"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "18dSqoEGyMkG",
        "colab_type": "text"
      },
      "source": [
        "**Identifying the type of data using info()**\n",
        "\n",
        "To identify the data types, I use the info method. The info method prints a summary of the data in the data frame along with its data types. Here, there are 428 entries (0-427 rows). The data frame after removing irrelevant columns comprises 10 columns. Here the Make, MSRP  are of an object type whereas Engine size and Cylinders are of float type and Horsepower, MPG_City, MPG_Highway, Weight, Wheelbase and Length are of integer type. Hence there are 2 object types, 2 float types and 6 integer types of data present in the data frame. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eCcllQlBJOTB",
        "colab_type": "code",
        "outputId": "bd5aa005-397c-4392-902b-2f8a7f978b1c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 277
        }
      },
      "source": [
        "# To identify the type of data\n",
        "df.info()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 428 entries, 0 to 427\n",
            "Data columns (total 10 columns):\n",
            "Make           428 non-null object\n",
            "MSRP           428 non-null object\n",
            "EngineSize     428 non-null float64\n",
            "Cylinders      426 non-null float64\n",
            "Horsepower     428 non-null int64\n",
            "MPG_City       428 non-null int64\n",
            "MPG_Highway    428 non-null int64\n",
            "Weight         428 non-null int64\n",
            "Wheelbase      428 non-null int64\n",
            "Length         428 non-null int64\n",
            "dtypes: float64(2), int64(6), object(2)\n",
            "memory usage: 33.5+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oZOMXN_5Q76_",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3QYs6qRzyTyK",
        "colab_type": "text"
      },
      "source": [
        "**Finding the dimensions of the data frame**\n",
        "\n",
        "To get the number of rows and columns of the data frame, I used the shape method. The shape method gets the number of rows and the number of columns of the data frame. Here, there are 428 rows and 10 columns. Hence the shape method returns (428, 10). And to find the dimensions of the data frame I used ndim (dimension) method. This method prints the dimensions of the data frame. Here, the whole data frame is of 2 dimensional (rows and columns).\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NK5lDFrlJQZ2",
        "colab_type": "code",
        "outputId": "191c87cc-c25e-40f0-ee25-d471b8b8dc3b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "# Getting the number of instances and features\n",
        "df.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(428, 10)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sWA_xeKfLpkl",
        "colab_type": "code",
        "outputId": "2dc3bedc-e11e-4b68-c207-97c19a726499",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "# Getting the dimensions of the data frame\n",
        "df.ndim"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7DttZwsVNXY5",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EqHaM8ASyXn0",
        "colab_type": "text"
      },
      "source": [
        "**Finding the duplicate data.**\n",
        "\n",
        "This is a handy thing to perform on a data set because often there might be duplicate or redundant data in the data sets, to remove this I used the MSRP as a reference such that there cannot be more than two same MSRP prices of the car, it shows that few data are redundant because prices of the cars can never match very accurately. So before removing the duplicates, there were 428 rows and after removing there are 410 meaning that there were 18 duplicate data."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZTsS4rHyJjrx",
        "colab_type": "code",
        "outputId": "e9912311-6a82-414f-b845-8f01689015a6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 208
        }
      },
      "source": [
        "df = df.drop_duplicates(subset='MSRP', keep='first')\n",
        "df.count()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Make           410\n",
              "MSRP           410\n",
              "EngineSize     410\n",
              "Cylinders      408\n",
              "Horsepower     410\n",
              "MPG_City       410\n",
              "MPG_Highway    410\n",
              "Weight         410\n",
              "Wheelbase      410\n",
              "Length         410\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sTIhMBTURIWg",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LGENLOYbydpl",
        "colab_type": "text"
      },
      "source": [
        "**Finding the missing or null values.**\n",
        "\n",
        "Many times there might be a lot of missing values in the dataset. There are several approaches to deal with this scenario either we can drop those values or fill those values with the mean of that column. Here, 2 entries were having N/A in the Cylinders feature. This can be found by using the is_null( ) method which returns the null or missing values in the data frame. So rather than deleting those two entries, I filled those values with the mean of the cylinders columns and their value came as 6.0 each. I was able to find this while I was peeking at the first and last few rows of the data set. I think rather than deleting this is a good approach because every entry of data is vital. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S9aII503Lncg",
        "colab_type": "code",
        "outputId": "3a687464-6212-41e0-c991-a6357a59ff78",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        }
      },
      "source": [
        "# To peek at first five rows\n",
        "df.head(5)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Make</th>\n",
              "      <th>MSRP</th>\n",
              "      <th>EngineSize</th>\n",
              "      <th>Cylinders</th>\n",
              "      <th>Horsepower</th>\n",
              "      <th>MPG_City</th>\n",
              "      <th>MPG_Highway</th>\n",
              "      <th>Weight</th>\n",
              "      <th>Wheelbase</th>\n",
              "      <th>Length</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Acura</td>\n",
              "      <td>$36,945</td>\n",
              "      <td>3.5</td>\n",
              "      <td>6.0</td>\n",
              "      <td>265</td>\n",
              "      <td>17</td>\n",
              "      <td>23</td>\n",
              "      <td>4451</td>\n",
              "      <td>106</td>\n",
              "      <td>189</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Acura</td>\n",
              "      <td>$23,820</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>200</td>\n",
              "      <td>24</td>\n",
              "      <td>31</td>\n",
              "      <td>2778</td>\n",
              "      <td>101</td>\n",
              "      <td>172</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Acura</td>\n",
              "      <td>$26,990</td>\n",
              "      <td>2.4</td>\n",
              "      <td>4.0</td>\n",
              "      <td>200</td>\n",
              "      <td>22</td>\n",
              "      <td>29</td>\n",
              "      <td>3230</td>\n",
              "      <td>105</td>\n",
              "      <td>183</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Acura</td>\n",
              "      <td>$33,195</td>\n",
              "      <td>3.2</td>\n",
              "      <td>6.0</td>\n",
              "      <td>270</td>\n",
              "      <td>20</td>\n",
              "      <td>28</td>\n",
              "      <td>3575</td>\n",
              "      <td>108</td>\n",
              "      <td>186</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Acura</td>\n",
              "      <td>$43,755</td>\n",
              "      <td>3.5</td>\n",
              "      <td>6.0</td>\n",
              "      <td>225</td>\n",
              "      <td>18</td>\n",
              "      <td>24</td>\n",
              "      <td>3880</td>\n",
              "      <td>115</td>\n",
              "      <td>197</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Make     MSRP  EngineSize  ...  Weight  Wheelbase  Length\n",
              "0  Acura  $36,945         3.5  ...    4451        106     189\n",
              "1  Acura  $23,820         2.0  ...    2778        101     172\n",
              "2  Acura  $26,990         2.4  ...    3230        105     183\n",
              "3  Acura  $33,195         3.2  ...    3575        108     186\n",
              "4  Acura  $43,755         3.5  ...    3880        115     197\n",
              "\n",
              "[5 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D7yCENDCxiHi",
        "colab_type": "code",
        "outputId": "c8e2650e-33f7-4280-93df-fcaba3ac86c7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        }
      },
      "source": [
        "# To peek at last five rows\n",
        "df.tail(5)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Make</th>\n",
              "      <th>MSRP</th>\n",
              "      <th>EngineSize</th>\n",
              "      <th>Cylinders</th>\n",
              "      <th>Horsepower</th>\n",
              "      <th>MPG_City</th>\n",
              "      <th>MPG_Highway</th>\n",
              "      <th>Weight</th>\n",
              "      <th>Wheelbase</th>\n",
              "      <th>Length</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>423</th>\n",
              "      <td>Volvo</td>\n",
              "      <td>$40,565</td>\n",
              "      <td>2.4</td>\n",
              "      <td>5.0</td>\n",
              "      <td>197</td>\n",
              "      <td>21</td>\n",
              "      <td>28</td>\n",
              "      <td>3450</td>\n",
              "      <td>105</td>\n",
              "      <td>186</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>424</th>\n",
              "      <td>Volvo</td>\n",
              "      <td>$42,565</td>\n",
              "      <td>2.3</td>\n",
              "      <td>5.0</td>\n",
              "      <td>242</td>\n",
              "      <td>20</td>\n",
              "      <td>26</td>\n",
              "      <td>3450</td>\n",
              "      <td>105</td>\n",
              "      <td>186</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>425</th>\n",
              "      <td>Volvo</td>\n",
              "      <td>$45,210</td>\n",
              "      <td>2.9</td>\n",
              "      <td>6.0</td>\n",
              "      <td>268</td>\n",
              "      <td>19</td>\n",
              "      <td>26</td>\n",
              "      <td>3653</td>\n",
              "      <td>110</td>\n",
              "      <td>190</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>426</th>\n",
              "      <td>Volvo</td>\n",
              "      <td>$26,135</td>\n",
              "      <td>1.9</td>\n",
              "      <td>4.0</td>\n",
              "      <td>170</td>\n",
              "      <td>22</td>\n",
              "      <td>29</td>\n",
              "      <td>2822</td>\n",
              "      <td>101</td>\n",
              "      <td>180</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>427</th>\n",
              "      <td>Volvo</td>\n",
              "      <td>$35,145</td>\n",
              "      <td>2.5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>208</td>\n",
              "      <td>20</td>\n",
              "      <td>27</td>\n",
              "      <td>3823</td>\n",
              "      <td>109</td>\n",
              "      <td>186</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      Make     MSRP  EngineSize  ...  Weight  Wheelbase  Length\n",
              "423  Volvo  $40,565         2.4  ...    3450        105     186\n",
              "424  Volvo  $42,565         2.3  ...    3450        105     186\n",
              "425  Volvo  $45,210         2.9  ...    3653        110     190\n",
              "426  Volvo  $26,135         1.9  ...    2822        101     180\n",
              "427  Volvo  $35,145         2.5  ...    3823        109     186\n",
              "\n",
              "[5 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4imDMry4xkt7",
        "colab_type": "text"
      },
      "source": [
        "While using both head and tail method I found out that there were two values stored a NaN (Not a number) in the Cylinders features. So I printed them by using the slicing technique of mentioning their index."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ILDnZYH9Jmbk",
        "colab_type": "code",
        "outputId": "f8eb13c6-d25f-48c7-96cd-6bb87604f7ed",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 208
        }
      },
      "source": [
        "# Finding the null values\n",
        "print(df.isnull().sum())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Make           0\n",
            "MSRP           0\n",
            "EngineSize     0\n",
            "Cylinders      2\n",
            "Horsepower     0\n",
            "MPG_City       0\n",
            "MPG_Highway    0\n",
            "Weight         0\n",
            "Wheelbase      0\n",
            "Length         0\n",
            "dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qv8hBVOvL7QR",
        "colab_type": "code",
        "outputId": "f66d0609-e4de-48e2-9221-cb0f572e2994",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 110
        }
      },
      "source": [
        "# Printing the null value rows\n",
        "df[240:242]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Make</th>\n",
              "      <th>MSRP</th>\n",
              "      <th>EngineSize</th>\n",
              "      <th>Cylinders</th>\n",
              "      <th>Horsepower</th>\n",
              "      <th>MPG_City</th>\n",
              "      <th>MPG_Highway</th>\n",
              "      <th>Weight</th>\n",
              "      <th>Wheelbase</th>\n",
              "      <th>Length</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>247</th>\n",
              "      <td>Mazda</td>\n",
              "      <td>$25,700</td>\n",
              "      <td>1.3</td>\n",
              "      <td>NaN</td>\n",
              "      <td>197</td>\n",
              "      <td>18</td>\n",
              "      <td>25</td>\n",
              "      <td>3053</td>\n",
              "      <td>106</td>\n",
              "      <td>174</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>248</th>\n",
              "      <td>Mazda</td>\n",
              "      <td>$27,200</td>\n",
              "      <td>1.3</td>\n",
              "      <td>NaN</td>\n",
              "      <td>238</td>\n",
              "      <td>18</td>\n",
              "      <td>24</td>\n",
              "      <td>3029</td>\n",
              "      <td>106</td>\n",
              "      <td>174</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      Make     MSRP  EngineSize  ...  Weight  Wheelbase  Length\n",
              "247  Mazda  $25,700         1.3  ...    3053        106     174\n",
              "248  Mazda  $27,200         1.3  ...    3029        106     174\n",
              "\n",
              "[2 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n6IdL0FIMImO",
        "colab_type": "code",
        "outputId": "28cf95e6-3933-4c27-826d-d26fda5711ae",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104
        }
      },
      "source": [
        "# Filling the rows with the mean of the column\n",
        "val = df['Cylinders'].mean()\n",
        "df['Cylinders'][247] = round(val)\n",
        "\n",
        "val = df['Cylinders'].mean()\n",
        "df['Cylinders'][248]= round(val)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "  \"\"\"\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "14YTFXLwRsNu",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EDhwqdvCyiJc",
        "colab_type": "text"
      },
      "source": [
        "**Converting the object values to integer type.**\n",
        "\n",
        "While having a look at the data, the MSRP was stored as an object type. This is a serious problem because it is impossible to plot those values on a graph because it is a primary requirement that during plotting a graph all the values must be of type integer data. The author has stored, the MSRP in a different format ($36, 000) so I had to remove the formatting and then convert them to an integer.\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AePvAc0mL8oU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Removing the formatting\n",
        "df['MSRP'] = [x.replace('$', '') for x in df['MSRP']] \n",
        "df['MSRP'] = [x.replace(',', '') for x in df['MSRP']]  "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9AoMNHNnMaC6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df['MSRP']=pd.to_numeric(df['MSRP'],errors='coerce')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tWXoT-PYVn3E",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CL1iNFB3GmQq",
        "colab_type": "text"
      },
      "source": [
        "**Detecting Outliers**\n",
        "\n",
        "An outlier is a point or set of points different from other points. Sometimes they can be very high or very low. It’s often a good idea to detect and remove the outliers. Because outliers are one of the primary reasons for resulting in a less accurate model. Hence it’s a good idea to remove them. I will perform the IQR score technique to detect and remove the outliers. Often outliers can be seen with visualizations using a box plot. Shown below is the box plot of MSRP. In the plot, you can find some points are outside the box they are none other than outliers. I referred the above outlier technique from towards data science article which can be found in the references section [3]."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i9t9l0VUVpDa",
        "colab_type": "code",
        "outputId": "d23f9169-406f-4926-bafb-a6e4d9afa64d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 309
        }
      },
      "source": [
        "sns.boxplot(x=df['MSRP'])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f1d524bbf28>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXUAAAESCAYAAAACDEUqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6ffSJshqUiSt",
        "colab_type": "code",
        "outputId": "e01a9be4-ea87-4155-99af-7be277bd24e0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 191
        }
      },
      "source": [
        "Q1 = df.quantile(0.25)\n",
        "Q3 = df.quantile(0.75)\n",
        "IQR = Q3 - Q1\n",
        "print(IQR)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "MSRP           19086.50\n",
            "EngineSize         1.55\n",
            "Cylinders          2.00\n",
            "Horsepower        85.00\n",
            "MPG_City           4.00\n",
            "MPG_Highway        5.00\n",
            "Weight           872.25\n",
            "Wheelbase          9.00\n",
            "Length            16.00\n",
            "dtype: float64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C46mgW5BUqik",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = df[~((df < (Q1 - 1.5 * IQR)) |(df > (Q3 + 1.5 * IQR))).any(axis=1)]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4ReduevDZlEA",
        "colab_type": "text"
      },
      "source": [
        "After using the technique now as seen below the MSRP box plot contains no outlier points this is a big improvement. Previously there were over 15 points of outliers now I have removed those outliers."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_7VWBG3vtPFV",
        "colab_type": "code",
        "outputId": "8e76057b-7ada-4c3a-f492-2898316ba910",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 309
        }
      },
      "source": [
        "sns.boxplot(x=df['MSRP'])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fd3e2a386d8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWkAAAESCAYAAAA/niRMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nDQCGEWkAMIxIA4BhRBoADPs/cJmDQN84MfsAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xnwk7E7HX68v",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uTO74i0vynyX",
        "colab_type": "text"
      },
      "source": [
        "**Performing a 5 number summary (min, lower quartile, median, upper\n",
        "quartile, max)**\n",
        "\n",
        "Next step is to perform a 5-number summary for the numeric data. As discussed earlier the numeric data, in this case, are MSRP, Engine Size, Horsepower, Cylinders, Horsepower, MPG_City, MPG_Highway, Weight, Wheelbase, and Length. The five-number summary includes minimum, lower quartile, median, upper quartile, and the maximum values all these values can be obtained by using the describe method.\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PpY10jNJMDYp",
        "colab_type": "code",
        "outputId": "518adad4-46e2-4d2b-dd63-79c1cf272fcb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        }
      },
      "source": [
        "df.describe()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>MSRP</th>\n",
              "      <th>EngineSize</th>\n",
              "      <th>Cylinders</th>\n",
              "      <th>Horsepower</th>\n",
              "      <th>MPG_City</th>\n",
              "      <th>MPG_Highway</th>\n",
              "      <th>Weight</th>\n",
              "      <th>Wheelbase</th>\n",
              "      <th>Length</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>324.000000</td>\n",
              "      <td>324.000000</td>\n",
              "      <td>324.000000</td>\n",
              "      <td>324.000000</td>\n",
              "      <td>324.000000</td>\n",
              "      <td>324.000000</td>\n",
              "      <td>324.000000</td>\n",
              "      <td>324.000000</td>\n",
              "      <td>324.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>29791.682099</td>\n",
              "      <td>3.102160</td>\n",
              "      <td>5.694444</td>\n",
              "      <td>209.777778</td>\n",
              "      <td>19.614198</td>\n",
              "      <td>26.583333</td>\n",
              "      <td>3541.876543</td>\n",
              "      <td>107.746914</td>\n",
              "      <td>186.259259</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>11024.164921</td>\n",
              "      <td>0.889513</td>\n",
              "      <td>1.308167</td>\n",
              "      <td>54.125642</td>\n",
              "      <td>2.914029</td>\n",
              "      <td>3.835128</td>\n",
              "      <td>564.457566</td>\n",
              "      <td>5.831814</td>\n",
              "      <td>11.569446</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>10280.000000</td>\n",
              "      <td>1.300000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>104.000000</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>17.000000</td>\n",
              "      <td>2403.000000</td>\n",
              "      <td>95.000000</td>\n",
              "      <td>158.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>21436.250000</td>\n",
              "      <td>2.400000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>170.000000</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>25.000000</td>\n",
              "      <td>3194.750000</td>\n",
              "      <td>104.000000</td>\n",
              "      <td>178.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>27635.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>208.000000</td>\n",
              "      <td>19.000000</td>\n",
              "      <td>26.000000</td>\n",
              "      <td>3470.000000</td>\n",
              "      <td>107.000000</td>\n",
              "      <td>187.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>36907.500000</td>\n",
              "      <td>3.500000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>240.000000</td>\n",
              "      <td>21.000000</td>\n",
              "      <td>29.000000</td>\n",
              "      <td>3837.000000</td>\n",
              "      <td>112.000000</td>\n",
              "      <td>193.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>65000.000000</td>\n",
              "      <td>5.700000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>350.000000</td>\n",
              "      <td>27.000000</td>\n",
              "      <td>36.000000</td>\n",
              "      <td>5270.000000</td>\n",
              "      <td>124.000000</td>\n",
              "      <td>215.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               MSRP  EngineSize  ...   Wheelbase      Length\n",
              "count    324.000000  324.000000  ...  324.000000  324.000000\n",
              "mean   29791.682099    3.102160  ...  107.746914  186.259259\n",
              "std    11024.164921    0.889513  ...    5.831814   11.569446\n",
              "min    10280.000000    1.300000  ...   95.000000  158.000000\n",
              "25%    21436.250000    2.400000  ...  104.000000  178.000000\n",
              "50%    27635.000000    3.000000  ...  107.000000  187.000000\n",
              "75%    36907.500000    3.500000  ...  112.000000  193.000000\n",
              "max    65000.000000    5.700000  ...  124.000000  215.000000\n",
              "\n",
              "[8 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OZ3Ym8dTanwE",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YE6-1fMhyrvF",
        "colab_type": "text"
      },
      "source": [
        "**Plotting different features against one another.**\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7ZaDhbDaabBq",
        "colab_type": "text"
      },
      "source": [
        "**Histogram**\n",
        "\n",
        "Histogram refers to the frequency of occurrence of variables in an interval. Here, there are mainly 10 different car manufacturing companies, but it is often important to know who has the maximum number of cars. I'm just plotting histograms to find the total number of car manufacturers and this plot does not support my predictions or doesn't have relations with the price feature. Plotting a histogram is one of a trivial solution which lets us know the total number of different car manufacturers. From the histogram below it can be seen that Ford has almost several cars (20) followed by Chevrolet (19) and many more."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4nvipLhkRHGa",
        "colab_type": "code",
        "outputId": "2062eb95-5c48-417d-9c14-af6d7d5d9820",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 431
        }
      },
      "source": [
        "# Plotting a Histogram\n",
        "df.Make.value_counts().nlargest(40).plot(kind='bar', figsize=(10,5))\n",
        "plt.title(\"Number of cars by make\")\n",
        "plt.ylabel('Number of cars')\n",
        "plt.xlabel('Make');"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAnUAAAGdCAYAAAB0CIUmAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qM7LTrDERaun",
        "colab_type": "text"
      },
      "source": [
        "**Heat Maps**\n",
        "\n",
        "Heat Maps is a plot which is necessary when we need to find the dependent variables. One of the best ways to find the correlation between the features can be done using heat maps. As shown below the price feature (MSRP) has a strong correlation with Horsepower of 83% this is very important because the more the relationship between the variables the more accurate the model will be. This is how the correlation between the features can be found using heat maps. With the help of heat maps I can use these related features in building my model."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9Li-PVqdRaG4",
        "colab_type": "code",
        "outputId": "a347ed3f-e488-4bbd-edc8-56a804286142",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 414
        }
      },
      "source": [
        "# Plotting a heat map\n",
        "plt.figure(figsize=(10,5))\n",
        "c= df.corr()\n",
        "sns.heatmap(c,cmap=\"BrBG\",annot=True)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f404c675908>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAnEAAAF7CAYAAAC9/t3DAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\njBoTL+MqAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 720x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f6AktmpeaEJO",
        "colab_type": "text"
      },
      "source": [
        "**Scatterplot between two related varirables**\n",
        "\n",
        "I know the features especially MSRP and the Horsepower are more related. Since I have two related variables I used a scatter plot to show their relationship. Here the scatter plots are plotted between Horsepower and MSRP are as shown below. With the plot given below, we can easily draw a trend line during modeling. I can easily see a line of best fit in the plot. I have not included the scatter plot between MSRP and Engine Size or Cylinders the reason for this is that these data have comparatively less correlation with the MSRP than that of MSRP and Horsepower which is 83%. Because as seen above the correlation between MSRP and Engine Size is of 54% and that of MSRP and Cylinders is of 64% so there is no reason to plot these features."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ub22V4yFaF9x",
        "colab_type": "code",
        "outputId": "d7745872-1d62-4fe2-c758-dc2043d7a8da",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 355
        }
      },
      "source": [
        "# Plotting a scatter plot\n",
        "fig, ax = plt.subplots(figsize=(5,5))\n",
        "ax.scatter(df['Horsepower'], df['MSRP'])\n",
        "plt.title('Scatter plot between MSRP and Horsepower')\n",
        "ax.set_xlabel('Horsepower')\n",
        "ax.set_ylabel('MSRP')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWgAAAFSCAYAAADfBEUeAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQBMEQagUEmiCIAiV8v8BuVQqSB0B1JMAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rZXtWvQFSyU8",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KJkZCM9ZxbVd",
        "colab_type": "text"
      },
      "source": [
        "## 5) Reporting Initial Findings\n",
        "\n",
        "I think there is a high relationship between the MSRP (Price) and the Horsepower feature of the car. I will explore more about that in assignment 4. Now I know my problem statement is “Predicting the price (MSRP) of the car given the specifications of the car”. The main idea is to predict the (MSRP) price of the car. Now I know that I have to predict a value so I should use Regression Algorithms because I have two related features (independent and dependent features). But there are many types of Regression Algorithms such as Linear Regression, Random Forest Regression, Lasso and Ridge Regression and many more. So I might use one of these algorithms and implement a machine learning model to predict the price in assignment 4. Hence this assignment which mainly deals with Exploratory Data Analysis where I prepared my data in such a way that it is now ready for building a model. \n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mhVbhIX0nLKH",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9DFM7f0TiGCn",
        "colab_type": "text"
      },
      "source": [
        "## References\n",
        "\n",
        "[1] Janjughazyan, L. (2017). Cars Data. [online] Kaggle.com. Available at: https://www.kaggle.com/ljanjughazyan/cars1 [Accessed 15 Aug. 2019].\n",
        "\n",
        "[2] Srinivasan, R. (2017). Automobile Dataset. [online] Kaggle.com. Available at: https://www.kaggle.com/toramky/automobile-dataset [Accessed 16 Aug. 2019].\n",
        "\n",
        "[3] Sharma, N. (2018). Ways to Detect and Remove the Outliers. [online] Medium. Available at: https://towardsdatascience.com/ways-to-detect-and-remove-the-outliers-404d16608dba [Accessed 15 Aug. 2019]."
      ]
    }
  ]
}